Markovian source separation

Abstract

A maximum likelihood (ML) approach is used to separate the instantaneous mixtures of temporally correlated, independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources. A Markov model is used to represent the joint probability density of successive samples of each source. The joint probability density functions are estimated from the observations using a kernel method. For the special case of autoregressive models, the theoretical performance of the algorithm is computed and compared with the performance of second-order algorithms and i.i.d.-based separation algorithms.